load_esol#

skfp.datasets.moleculenet.load_esol(data_dir: str | PathLike | None = None, as_frame: bool = False, verbose: bool = False) → DataFrame | tuple[list[str]] | ndarray#

Load and return the ESOL (Estimated SOLubility) dataset.

The task is to predict aqueous solubility [1] [2]. Targets are log-transformed, and the unit is log mols per litre (log Mol/L).

Tasks	1
Task type	regression
Total samples	1128
Recommended split	scaffold
Recommended metric	RMSE

Parameters:

data_dir ({None, str, path-like}, default=None) – Path to the root data directory. If None, currently set scikit-learn directory is used, by default $HOME/scikit_learn_data.
as_frame (bool, default=False) – If True, returns the raw DataFrame with columns: “SMILES”, “label”. Otherwise, returns SMILES as list of strings, and labels as a NumPy array (1D float vector).
verbose (bool, default=False) – If True, progress bar will be shown for downloading or loading files.

Returns:

data – Depending on the as_frame argument, one of: - Pandas DataFrame with columns: “SMILES”, “label” - tuple of: list of strings (SMILES), NumPy array (labels)

Return type:

pd.DataFrame or tuple(list[str], np.ndarray)

References

Examples

>>> from skfp.datasets.moleculenet import load_esol
>>> dataset = load_esol()
>>> dataset  
(['OCC3OC(OCC2OC(OC(C#N)c1ccccc1)C(O)C(O)C2O)C(O)C(O)C3O ', ..., 'COP(=O)(OC)OC(=CCl)c1cc(Cl)c(Cl)cc1Cl'],
    array([-0.77 , -3.3  , -2.06 , ..., -3.091, -3.18 , -4.522]))

>>> dataset = load_esol(as_frame=True)
>>> dataset.head() 
                                              SMILES  label
0  OCC3OC(OCC2OC(OC(C#N)c1ccccc1)C(O)C(O)C2O)C(O)...  -0.77
1                             Cc1occc1C(=O)Nc2ccccc2  -3.30
2                               CC(C)=CCCC(C)=CC(=O)  -2.06
3                 c1ccc2c(c1)ccc3c2ccc4c5ccccc5ccc43  -7.87
4                                            c1ccsc1  -1.33

load_esol#

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